scholarly journals Quantum-inspired feature and parameter optimisation of evolving spiking neural networks with a case study from ecological modeling

2009 ◽  
Author(s):  
Stefan Schliebs ◽  
Michael Defoin Platel ◽  
Sue Worner ◽  
Nikola Kasabov
Keyword(s):  
Neural Networks ◽  
Ecological Modeling ◽  
Spiking Neural Networks ◽  
Parameter Optimisation

scholarly journals Evolving spiking neural networks methods for classification problem: a case study in flood events risk assessment

2019 ◽  
Vol 16 (1) ◽  
pp. 222 ◽  
Author(s):  
Mohd Hafizul Afifi Abdullah ◽  
Muhaini Othman ◽  
Shahreen Kasim ◽  
Siti Aisyah Mohamed
Keyword(s):  
Neural Networks ◽  
Data Model ◽  
Historical Data ◽  
Classification Problem ◽  
Spiking Neural Networks ◽  
Working Mechanism ◽  
Specific Data ◽  
Environmental Events ◽  
The Brain

<p>Analysing environmental events such as predicting the risk of flood is considered as a challenging task due to the dynamic behaviour of the data. One way to correctly predict the risk of such events is by gathering as much of related historical data and analyse the correlation between the features which contribute to the event occurrences. Inspired by the brain working mechanism, the spiking neural networks have proven the capability of revealing a significant association between different variables spike behaviour during an event. Personalised modelling, on the other hand, allows a personal model to be created for a specific data model and experiment. Therefore, a personalised modelling method incorporating spiking neural network is used to create a personalised model for assessing a real-world flood case study in Kuala Krai, Kelantan based on historical data of 2012-2016 provided by Malaysian Meteorological Department. The result shows that the method produces the highest accuracy among the selected compared algorithms.</p>

The Simulation of Spiking Neural Networks

2010 ◽  
pp. 337-358 ◽  
Author(s):  
David Gamez
Keyword(s):  
Neural Networks ◽  
Recent Work ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Spiking Neural Networks ◽  
Future Trends ◽  
Neuron Models ◽  
Event Simulation ◽  
The Brain

This chapter is an overview of the simulation of spiking neural networks that relates discrete event simulation to other approaches and includes a case study of recent work. The chapter starts with an introduction to the key components of the brain and sets out three neuron models that are commonly used in simulation work. After explaining discrete event, continuous and hybrid simulation, the performance of each method is evaluated and recent research is discussed. To illustrate the issues surrounding this work, the second half of this chapter presents a case study of the SpikeStream neural simulator that covers the architecture, performance and typical applications of this software along with some recent experiments. The last part of the chapter suggests some future trends for work in this area.

scholarly journals Quantum-Inspired Features and Parameter Optimization of Spiking Neural Networks for a Case Study from Atmospheric

2015 ◽  
Vol 53 ◽  
pp. 74-81 ◽  
Author(s):  
Marcelo C. Cardoso ◽  
Marco Silva ◽  
Marley M.B.R. Vellasco ◽  
Edson Cataldo
Keyword(s):  
Neural Networks ◽  
Parameter Optimization ◽  
Spiking Neural Networks

scholarly journals Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

2014 ◽  
Vol 134 ◽  
pp. 269-279 ◽  
Author(s):  
Nikola Kasabov ◽  
Valery Feigin ◽  
Zeng-Guang Hou ◽  
Yixiong Chen ◽  
Linda Liang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Temporal Patterns ◽  
Spiking Neural Networks ◽  
Spatio Temporal
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